In order to increase the quality of computer tomographic recording series, it is known to process image data sets that have already been reconstructed. The document DE 10 2005 038 940 A1 may for example be cited, in which an edge-preserving filter is used for image enhancement of the reconstructed image data sets. In the publication P. Perona and J. Malik, Scale space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, pp. 629-639, 1990; J. Weichert, Anisotropic Diffusion Filtering in Image Processing, Teubner-Verlag, Stuttgart, Germany, 1998, diffusion filters are applied to reconstructed image data in order to improve the image quality. The document DE 10 2005 012 654 A1 may furthermore be cited, in which image data are filtered by using correlation calculations, here again in order to produce a quality improvement.
However, all these known methods for increasing the quality of computer image recordings by image data processing reach their limitations when the relevant contrast is close to or less than the noise. If CT perfusion scans of particular organs are considered, for example the brain, liver or heart, then it is found that the typical CT value changes which are needed in order to detect the perfusion lie in the range of about 2 to 20 HU, i.e. 0.2 to 2% of the contrast of water against air. The pixel noise therefore plays a crucial role.
Another problem is that each of the methods requires reconstruction of the image data sets before the data material is processed, which entails a relatively high computation cost.